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pretrain_bert.py
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pretrain_bert.py
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# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http:https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pretrain BERT"""
import torch
import torch.nn.functional as F
from megatron import get_args
from megatron import get_timers
from megatron import mpu
from megatron import print_rank_0
from megatron.data.bert_dataset import build_train_valid_test_datasets
from megatron.model import BertModel
from megatron.training import pretrain
from megatron.utils import make_data_loader
from megatron.utils import reduce_losses
def model_provider():
"""Build the model."""
args = get_args()
print_rank_0('building BERT model ...')
model = BertModel(
num_tokentypes=2,
add_binary_head=True,
parallel_output=True)
return model
def get_batch(data_iterator):
# Items and their type.
keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask']
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens = data_b['text'].long()
types = data_b['types'].long()
sentence_order = data_b['is_random'].long()
loss_mask = data_b['loss_mask'].float()
lm_labels = data_b['labels'].long()
padding_mask = data_b['padding_mask'].long()
return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
def forward_step(data_iterator, model):
"""Forward step."""
timers = get_timers()
# Get the batch.
timers('batch generator').start()
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \
= get_batch(data_iterator)
timers('batch generator').stop()
# Forward model.
lm_logits, sop_logits = model(tokens, padding_mask, tokentype_ids=types)
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).contiguous().float(),
sentence_order.view(-1).contiguous(),
ignore_index=-1)
lm_loss_ = mpu.vocab_parallel_cross_entropy(lm_logits.contiguous().float(),
lm_labels.contiguous())
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
loss = lm_loss + sop_loss
reduced_losses = reduce_losses([lm_loss, sop_loss])
return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]}
def get_train_val_test_data():
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
args = get_args()
(train_data, valid_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
print_rank_0('> building train, validation, and test datasets '
'for BERT ...')
data_parallel_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * data_parallel_size
# Number of train/valid/test samples.
train_iters = args.train_iters
eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [train_iters * global_batch_size,
eval_iters * global_batch_size,
test_iters * global_batch_size]
print_rank_0(' > datasets target sizes (minimum size):')
print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))
print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))
print_rank_0(' test: {}'.format(train_val_test_num_samples[2]))
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
data_prefix=args.data_path,
data_impl=args.data_impl,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
max_seq_length=args.seq_length,
masked_lm_prob=args.mask_prob,
short_seq_prob=args.short_seq_prob,
seed=args.seed,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating BERT datasets ...")
train_data = make_data_loader(train_ds)
valid_data = make_data_loader(valid_ds)
test_data = make_data_loader(test_ds)
do_train = train_data is not None and args.train_iters > 0
do_valid = valid_data is not None and args.eval_iters > 0
do_test = test_data is not None and args.eval_iters > 0
# Need to broadcast num_tokens and num_type_tokens.
flags = torch.cuda.LongTensor(
[int(do_train), int(do_valid), int(do_test)])
else:
flags = torch.cuda.LongTensor([0, 0, 0])
# Broadcast num tokens.
torch.distributed.broadcast(flags,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
args.do_train = flags[0].item()
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
return train_data, valid_data, test_data
if __name__ == "__main__":
pretrain(get_train_val_test_data, model_provider, forward_step,
args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})